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In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.
There are a couple of things that I really liked about this book.
1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .
2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.
3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.
Overall I would say that this book helped me and that I learnt a bunch of new things.
If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.
- I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.
- Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.
I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.
Reasons why you shouldn't buy this book:
Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).
I have also included some pictures.
Great Book. Highly Recommend it!
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Customers say
Customers find the book to be a great introduction to machine learning with Python, offering a nice blend of theory and practice and useful code examples. Moreover, the writing style is well-received, and the book is well-laid out. However, the readability receives mixed feedback, with some finding it easy to understand while others say it's difficult to follow. Additionally, customers disagree on the depth of the content.
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59 customers mention content, 54 positive, 5 negative
Great book! I would recommend buying this as PDF directly from packtpub. There's a lot of code, and it can get hairy in Kindle format....Read more
Good book!...Read more
...It's an excellent book where you can find easy to understand explanation to most important concepts in machine learning....Read more
Probably the best book available in market for ML for programmer....Read more
51 customers mention practical, 44 positive, 7 negative
...It's a great resource for someone who wants to learn about machine learning but doesn't know where to start....Read more
I just started working on this book. It's very practical with well written examples and concise python codes....Read more
Very useful resource. Should be on the bookshelf of every Python developer doing Machine Learning.Read more
Good place to start. But I got fed up with the constant "no room to discuss this more, go read the literature".Read more
14 customers mention code, 13 positive, 1 negative
This book has a lot to offer both in the way of theory and code....Read more
...It also has a lot of code examples (for basically everything)....Read more
...The author does have an iPython notebook repo, which makes following the code much nicer; but for reading in bed, not needing to switch back-n-forth...Read more
...The codes are pretty helpful but at some point I got weary of reading about how to implement pre-written packages of the algorithms instead of...Read more
10 customers mention writing style, 10 positive, 0 negative
It's a good and well written book, covering a lot of different topics. It also has a lot of code examples (for basically everything)....Read more
Love it! Clearly written with nice examples....Read more
Very well written, was a little past my ability as a coder but was able to digest the concepts and understand it very well. Great resource.Read more
This text is really well written, but I would not advise someone who isn't intimately familiar with Python to jump straight into this book.Read more
7 customers mention complexity, 6 positive, 1 negative
The best thing about this book is that it presents fairly complex ideas with just enough mathematical detail to keep you intrigued, without turning...Read more
...The idea behind using ipython is good, but it would be better to include a small section for people who know python but never heard of ipython.Read more
...The author uses plain language and excellent alternative ways to think about key concepts in addition to including the underlying mathematics....Read more
...The charts/plots themselves are okay; they seem to be implemented in matplotlib, our go-to Python plotting library....Read more
7 customers mention page layout, 5 positive, 2 negative
...files that accompany the book using Anaconda extremely useful and well laid out....Read more
The book is very well organized, well written and has good on-line resources for downloading quality examples....Read more
...Then you click to go back to page. This horrible formatting makes reading the book tiring and frustrating....Read more
...Finally got the replacement from Amazon. This one is in perfect shape, i.e., no alignment issues at all. Woohoo! Just as promised....Read more
28 customers mention readability, 19 positive, 9 negative
Very well explained Step by step. Easy to followRead more
...I found the explanations of machine learning quite clear and easily understandable...Read more
...This is not a beginner's book, and I'm confused by people who are surprised at that - it says as much in the preface....Read more
Excellent, detailed intro into ML. Examples are simple, but understandable. I downloaded book's code from the website listed....Read more
6 customers mention depth, 4 positive, 2 negative
...Some exploration of GPU computing, with enough detail to permit the reader to experiment with this. Bad: -...Read more
It's a okay book. Though not much depth.Read more
...blend (for me at least) of theory and practice, as well as breadth and depth....Read more
...The breadth of information is immense, and the depth on each topic is enough that I would consider this a great scikit-learn reference....Read more
Great Book.
In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics. There are a couple of things that I really liked about this book. 1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ . 2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy. 3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it. Overall I would say that this book helped me and that I learnt a bunch of new things. If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience. - I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more. - Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off. I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem. Reasons why you shouldn't buy this book: Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case). I have also included some pictures. Great Book. Highly Recommend it!
Top reviews from the United States
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- Reviewed in the United States on October 10, 2016Format: PaperbackVerified Purchase
In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.
There are a couple of things that I really liked about this book.
1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .
2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.
3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.
Overall I would say that this book helped me and that I learnt a bunch of new things.
If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.
- I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.
- Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.
I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.
Reasons why you shouldn't buy this book:
Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).
I have also included some pictures.
Great Book. Highly Recommend it!
Images in this review
Top reviews from other countries
- Y ZhaoReviewed in Canada on April 13, 2017
great book for ML practitioners
Format: PaperbackVerified Purchase -
-
stefano fedeleReviewed in Italy on August 25, 2018
prima volta con machine learning
Format: PaperbackVerified Purchase - Daniel ManReviewed in the United Kingdom on March 17, 2016
Great Introduction to Machine Learning with Scikit-Learn
Format: PaperbackVerified Purchase
